Conferences related to Computational Neuroscience

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Recent efforts in computer vision have demonstrated impressive successes on a variety of real-world challenges. WACV conferences provide a forum for computer vision researchers working on practical applications to share their latest developments. WACV 2020 solicits high-quality, original submissions describing research on computer vision applications. Unlike other vision conferences, WACV emphasizes papers on systems and applications with significant, interesting vision components. Authors are encouraged to submit applications papers, as well as more traditional algorithms papers.

  • 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

    WACV conferences provide a forum for computer vision researchers working on practical applications to share their latest developments. WACV 2017 solicits high-quality, original submissions describing research on computer vision applications. Unlike other vision conferences, WACV emphasizes papers on systems and applications with significant, interesting vision components.

  • 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

    WACV brings together algorithm developers, software engineers, program managers and othersinterested in applied computer vision.

  • 2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

    WACV brings together algorithm developers, software engineers, program managers and others interested in applied computer vision.

  • 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

    WACV brings together algorithm developers, software engineers, program managers and others interested in applied computer vision.

  • 2015 IEEE Winter Conference on Applications of Computer Vision (WACV)

    Conference Scope: Computer Vision has become increasingly important in real world systems for commercial, industrial and military applications. Computer Vision related technologies have migrated from academic institutions to industrial laboratories, and onward into deployable systems. The goal ofthis workshop is to bring together an international cadre of academic, industrial, and government researchers, along with companies applying vision techniques.

  • 2014 IEEE Winter Conference on Applications of Computer Vision (WACV)

    Conference Scope: Computer Vision has become increasingly important in real world systems forcommercial, industrial and military applications. Computer Vision related technologies have migrated fromacademic institutions to industrial laboratories, and onward into deployable systems. The goal of thisworkshop is to bring together an international cadre of academic, industrial, and government researchers,along with companies applying vision techniques.

  • 2013 IEEE Workshop on Applications of Computer Vision (WACV)

    Computer Vision has become increasingly important in real world systems for commercial, industrial and military applications. Computer Vision related technologies have migrated from academic institutions to industrial laboratories, and onward into deployable systems. The goal of this workshop is to bring together an international cadre of academic, industrial, and government researchers, along with companies applying vision techniques.

  • 2012 IEEE Workshop on Applications of Computer Vision (WACV)

    Computer Vision has become increasingly important in real world systems for commercial, industrial and military applications. Computer Vision related technologies have migrated from academic institutions to industrial laboratories, and onward into deployable systems. The goal of this workshop is to bring together an international cadre of academic, industrial, and government researchers, along with companies applying vision techniques.

  • 2011 IEEE Workshop on Applications of Computer Vision (WACV)

    Computer Vision has become increasingly important in real world systems for commercial, industrial and military applications. Computer Vision related technologies have started migrating from academic institutions to industrial laboratories, and onward into deployable systems. The goal of this workshop is to bring together an international cadre of academic, industrial, and government researchers, and companies applying vision techniques


2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

One of the flagship conferences for the IEEE Robotics and Automation Society (RAS)


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


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Periodicals related to Computational Neuroscience

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


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Most published Xplore authors for Computational Neuroscience

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Xplore Articles related to Computational Neuroscience

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How computational neuroscience could help improving face recognition systems?

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE), 2014

Computational neuroscience is a growing discipline in science, which tries to understand the operations of human brain and inspire from it as a new computational paradigm. Face recognition is an important question both in pattern recognition and neuroscience. In the last few years, neuroscientists found many facts about object recognition in primate's brain. Here, we propose a cortex inspired face ...


A new flat interface nerve electrode design scheme based on finite element method, genetic algorithm and computational neuroscience method

IEEE Transactions on Magnetics, 2006

A study of motor nerve stimulation by flat interface nerve electrodes (FINE) using a new cuff electrode design based on finite element method, genetic algorithm and computational neuroscience method is presented. The purpose of this study is to obtain optimized parameters of electrode model for higher motor nerve stimulation efficiency. In this paper, the application for the focus stimulation on ...


CIRCUIT summer program: A computational neuroscience outreach experience for high-achieving undergraduates via sponsored research

2018 IEEE Integrated STEM Education Conference (ISEC), 2018

Programs that focus on student outreach are often disjoint from sponsored research efforts, despite the mutually beneficial opportunities that are possible with a combined approach. We designed and piloted a program to simultaneously meet the needs of underserved students and a large-scale sponsored research goal. Our program trained undergraduates to produce neuron maps for a major connectomics effort (i.e., single ...


CB: Exploring neuroscience with a humanoid research platform

2008 IEEE International Conference on Robotics and Automation, 2008

In this video presentation we introduce a 50 degrees of freedom humanoid robot, CB - <i>Computational</i> <i>Brain</i> [1]. CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real world contexts, as humans do. In so doing, we focus on utilising a system that ...


Computational Neuroscience and Multiple-Valued Logic

2009 39th International Symposium on Multiple-Valued Logic, 2009

Here, I explain possible communications between computational neuroscience and multiple-valued logic. I postulate that this "Understanding the Brain by Creating the Brain" approach is the only way to fully understand neural mechanisms in a rigorous sense.


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Educational Resources on Computational Neuroscience

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IEEE-USA E-Books

  • How computational neuroscience could help improving face recognition systems?

    Computational neuroscience is a growing discipline in science, which tries to understand the operations of human brain and inspire from it as a new computational paradigm. Face recognition is an important question both in pattern recognition and neuroscience. In the last few years, neuroscientists found many facts about object recognition in primate's brain. Here, we propose a cortex inspired face recognition system which uses some findings about the brain such as the operations of feature extractor cells in visual cortex and the function of attention in discarding distracting parts of the images. Now it is the time to merge the knowledge of learning systems with biological findings. The proposed method named Advanced Neurologically Inspires Face recognition (ANIF) system is compared with previous model NIF and some well- known face recognition algorithms within different datasets, which shows remarkable results.

  • A new flat interface nerve electrode design scheme based on finite element method, genetic algorithm and computational neuroscience method

    A study of motor nerve stimulation by flat interface nerve electrodes (FINE) using a new cuff electrode design based on finite element method, genetic algorithm and computational neuroscience method is presented. The purpose of this study is to obtain optimized parameters of electrode model for higher motor nerve stimulation efficiency. In this paper, the application for the focus stimulation on target fascicle is presented

  • CIRCUIT summer program: A computational neuroscience outreach experience for high-achieving undergraduates via sponsored research

    Programs that focus on student outreach are often disjoint from sponsored research efforts, despite the mutually beneficial opportunities that are possible with a combined approach. We designed and piloted a program to simultaneously meet the needs of underserved students and a large-scale sponsored research goal. Our program trained undergraduates to produce neuron maps for a major connectomics effort (i.e., single synapse brain maps), while providing these students with the resources and mentors to conduct novel research. Students were recruited from Johns Hopkins University to participate in a ten-week summer program. These students were trained in computational research, scientific communication skills and methods to map electron microscopy volumes. The students also had regular exposure to mentors and opportunities for guided, small group, independent discovery. A Learning-for- Use model was leveraged to provide the students with the tools, skills, and knowledge to pursue their research questions, while an Affinity Research Group model was adapted to provide students with mentorship in conducting cutting- edge research. A focus was placed on recruiting students who had limited opportunities and access to similar experiences. Program metrics demonstrated a substantial increase in knowledge (e.g., neuroscience, graph theory, machine learning, and scientific communication), while students also showed an overall increase in awareness and responsiveness to computational research after the program. Ultimately, the program positively impacted students' career choices and research readiness, and successfully achieved sponsor goals in a compact timeframe. This framework for combining outreach with sponsored research can be broadly leveraged for other programs across domains.

  • CB: Exploring neuroscience with a humanoid research platform

    In this video presentation we introduce a 50 degrees of freedom humanoid robot, CB - <i>Computational</i> <i>Brain</i> [1]. CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real world contexts, as humans do. In so doing, we focus on utilising a system that is closer to humans - in sensing, kinematics configuration and performance. We present a full-body compliance controller that was developed for the motion control of our humanoid robot [2]. Our initial experimentation on our system includes: 1) full-body compliant control - physical interactions/balancing/motion control; 2) the integrated visual ocular-motor responses; 3) perception and control - reaching, foveation, and active object recognition; 4) our studies of Central Pattern Generator for walking.

  • Computational Neuroscience and Multiple-Valued Logic

    Here, I explain possible communications between computational neuroscience and multiple-valued logic. I postulate that this "Understanding the Brain by Creating the Brain" approach is the only way to fully understand neural mechanisms in a rigorous sense.

  • Coding brain neurons via electrical network models for neuro-signal synthesis in computational neuroscience

    This paper develops a methodology for modeling and analysis of neural excitability that forms one of the most extensively studied mathematical frameworks in computational neuroscience. This framework is described by a set of differential equations known as Hodgkin-Huxley model and it synthesizes the influence of ionic currents on the cell voltage. The electrical equivalent circuit and the derivation of the conductance-based model of a neuron is based on a mathematical model that utilizes Hodgkin-Huxley equations which are considered as the single most influential finding in the biophysical description of excitable membranes to implement the current research in neurosciences. The neuron response with varying currents is demonstrated through analytical results and numerical simulations. The investigations in this paper lay the foundation for further deeper study and higher-order network models that can help eventually, through simulation and prediction, in the therapeutic treatment of brain diseases such as Alzheimer and Parkinson.

  • Optimization of cochlear implant electrode array using genetic algorithms and computational neuroscience models

    Finite-element (FE) analysis is used to compute the current distribution of the human cochlea during cochlear implant electrical stimulation. Genetic algorithms are then applied in conjunction with computational neuroscience models and FE analysis to optimize the shape and dimensions of cochlear implant electrode array. The goal is to improve the focus of electrical energy delivered to the auditory nerves in the human cochlea, thus reducing energy wasted and improve the efficiency and effectiveness of the cochlear implant system.

  • Computational Neuroscience as a Service: Porting MIIND to the Cloud

    In this paper, we investigate how cloud computing could benefit computational neuroscience. To that end, Multiple Interacting Instantiations of Neuronal Dynamics (MIIND), a computational neuroscience modelling toolkit, was ported to a private, university-owned cloud. The aim was to pave the way for making MIIND more accessible to non-specialist users in virtue of concealing its implementation context as well as rendering local IT infrastructure unnecessary. For that purpose, a customisable MIIND-based workflow model was encased within a virtualised wrapping apparatus. This served to fully automate running configurable MIIND simulations remotely via a convenient web-interface in a transparent manner with the user being incognisant of the cloud and the service orientated architecture behind it. This architecture can be adopted for any application conforming to the workflow characteristics of MIIND and helps inform the porting process of serial and legacy applications.

  • Initial Steps in Analyzing Science Gateways Sustainability through Business Model Canvas: A Use Case for the Computational Neuroscience Gateway

    The sustainability of science gateways has been a topic of active discussion. As successful research projects come to an end, it is necessary to find (new) models to secure continuous exploitation of products generated by these projects. Taking this step requires business considerations that are not trivial to do from the role of a researcher. In this paper we present our experiences in adopting the Business Model Canvas (BMC) methodology to reflect upon the sustainability of the science gateways designed, developed and operated by our group. This experience shows that the BMC helps to structure the various aspects to be considered, provides an excellent overview, and facilitates exploring alternative business models. We believe that this tool could be valuable also for other small groups that are facing similar challenges.

  • A novel face recognition system inspired by computational neuroscience

    The human brain operates superior than machines in most of the situations. Computational neuroscientists try to translate brain functions into the language of mathematics then modeling and implementing them into a machine. We can introduce novel methods and technologies inspired by the functions of the human brain. One of these technologies is behind the incredible power of human face recognition. Here we introduce a novel method for face recognition. In this method, we modeled the simple cells in the visual cortex (which are responsible for orientation selectivity) with the Directional Filter Banks. Then in order to normalize the illumination, we used the Single Scale Retinex. After that, for the other regions of the visual cortex (V2, V4, PIT...), we used the well known HMAX model of object recognition. After these feature extraction levels we need a classifier. The PFC area in the cortex performs like a classifier. In this stage we introduced a sparse representation based classifier by solving the L-1 regularized least square problem. Experimental results on facial image databases with varying illumination and pose shows that the performance of our model is comparable with well known methods in automatic face recognition.



Standards related to Computational Neuroscience

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Jobs related to Computational Neuroscience

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